Systematically uncovering the absorbed effective substances of Radix Scutellaria-licorice drug pair in rat plasma against COVID-19 using a combined UHPLC-Q-TOF-MS analysis and target network pharmacology

Radix Scutellaria-Licorice drug pair (RSLDP), a frequently used herbal pair with the effect of clearing heat and detoxifying, is the commonly employed drug pair in TCM prescriptions for the treatment of COVID-19. Until now, the metabolism feature and anti-COVID-19 mechanism of RSLDP have not been fully elucidated. In this study, a sensitive and rapid method was developed for the separation and identification of the absorbed constituents of RSLDP in the rat plasma by UHPLC-QTOF-MS. Additionally, we optimized the conventional methodologies of network pharmacology and proposed a new concept called target network pharmacology (T-NP). It used the absorbed constituents and the corresponding targets to generate a compound-target network, and compared to conventional network pharmacology, it could reduce false-positive results. A total of 85 absorbed constituents were identified or tentatively characterized in dosed plasma, including 32 components in the group of Radix Scutellaria, 27 components in the group of Licorice, and 65 components in the group of RSLDP. The results showed that the compatibility of Radix Scutellaria and Licorice increased the number of components in vivo. We found that 106 potential targets among the 61 active compounds in RSLDP were related to COVID-19. And 12 targets (STAT3, AKT1, EGFR, HSP9AA1, MAPK3, JUN, IL6, VEGFA, TNF, IL2, RELA, and STAT1) could be core targets for RSLDP in treating COVID-19. Results from these targets indicate that RSLDP treatment of COVID-19 mainly involves response to chemical stress, response to oxygenates, positive regulation of cytokines, PI3K-Akt signaling pathway, AGE-RAGE signaling pathway for diabetic complications, virus-related pathways such as novel coronavirus and human cytomegalovirus infection, inflammatory immune-related pathways, and so on. The metabolism feature of RSLDP in vivo was systematically uncovered. The combined use of the T-NP method could discover potential drug targets and disclose the biological processes of RSLDP, which will clarify the potential mechanisms of RSLDP in the treatment of COVID-19.

of RSLDP, which will clarify the potential mechanisms of RSLDP in the treatment of  to TCM research, has provided insight into the pharmacological mechanisms of drug action from the standpoint of macroscopic or holistic regulation among drugs, targets, and diseases, which aligns with the overall view of TCM [19]. Often, the absorbed ingredients are assumed to be effective, exerting their therapeutic effects in vivo [20]. However, the drawbacks of traditional network pharmacology, such as plenty of targets but low bioavailability of substances, lead to false-positive results. Therefore, we introduce target network pharmacology (T-NP), which is used to discover the potential therapeutic effects of absorbed components [21]. Therefore, we attempted to use the T-NP approach to analyze and clarify the pharmacological mechanisms of RSLDP. This is, to our knowledge, the first time to investigate the comprehensive results of the absorbed effective substances, the metabolic pathways, and the target pharmacological network analysis of RSLDP in vivo for the treatment of COVID-19.

Reagents and materials
Acetonitrile, methanol, and formic acid were of LC/MS grade obtained from the Tedia Company (USA). The ultrapure water used for the mobile phase was purchased from Wahaha Group Co., Ltd. (Hangzhou, China). The other reagents were of analytical grade. Wogonoside (wkq21031811), wogonin (wkq21022605), isoliquiritigenin (wkq21050610), Glycyrrhizic acid (wkq21012606), and Glycyrrhetinic acid (wkq21050707) were all supplied by the Sichuan Weikeqi Biological Technology Co., Ltd (Sichuan, PR China). The purity of the five substances was more than 98%. Radix Scutellariae and licorice were purchased from Bozhou (Anhui province, China). All the herbs were identified by Professor Jianhua Wang and kept in the Department of Pharmaceutical Analysis, School of Pharmacy, Hebei Medical University in Shijiazhuang, China.

Preparation of RS, licorice and RSLDP
Radix Scutellariae (20 g) and licorice (20 g) were mixed with 1:1 ratio, then immersed in water (1:10, w/v) for 0.5 h and refluxed for 1.5 h. The aqueous extract was filtered, and the dregs were refluxed again with water (1:8, w/v) under the same conditions. The two extraction solutions were combined and concentrated to 0.6 g mL −1 . The resulting extract was centrifuged at 13000 rpm for 10 min, then filtered through a 0.22 μm membrane for UHPLC-QTOF-MS analysis. The treated Radix Scutellariae (20 g) and licorice (20 g) alone were processed in the same way.

Animals and drug administration
Twelve male Sprague-Dawley (SD) rats (weights 220±20 g) were provided by the Laboratory Animal Center of Hebei Medical University (Shijiazhuang, China, License number: SCXK (Liao) 2020-0001). The rats were housed in controlled environmental conditions of 20±2˚C with 12 h light/dark cycles for one week prior to the experiment. Rats were fasted with free access to water for 12 h ahead of the experiment. All the experimental procedures were in accordance with the guidelines approved by the Animal Facility Guidelines of Hebei Medical University.
All the rats were randomly divided into four groups (n = 3): Group 1, the RS group; Group 2, the Licorice group; Group 3, the RSLDP group; and Group 4, the blank group. The herb extracts (6 g/kg) were orally administered to the corresponding experimental group. The rats in the blank group were given physiological saline at the same volume.

Plasma collection and preparation
The blood samples were collected in a 5 mL eppendorf tube with 1% heparin sodium via the orbital venous plexus from each rat at 0.25, 0.5, 1, 2, 4, 8, 12, and 24 h after oral administration and were centrifuged at 3500 rpm at 4˚C for 10 min to obtain plasma. Plasma samples from the different time points in the same group were combined into one sample and stored at -80˚C for further analysis. 500 μL plasma was treated with 1500 μL methanol, vortexed for 5 min and centrifuged at 13000 rpm for 10 min at 4˚C to remove the protein. Each sample was independently extracted three times. Next, the mixed supernatant was evaporated to dryness under N 2 flow at 37˚C, and residues were reconstituted in 500 μL solution of acetonitrile and centrifuged at 13000 rpm for 10 min. Finally, 5 μL of supernatant was used for UHPLC-Q-TOF-MS analysis.
The mass detection was operated on a Triple TOF 5600 + system (AB Sciex, Redwood, CA, USA) with Duo-Spray ion sources operating in the negative. The optimized mass spectrometer parameters were used as follows: -4.5 kV ion spray voltage, 550˚C turbo spray temperature, and 20 eV collision energy spread (CES). 35 psi curtain gas, 55 psi nebulizer gas (GS1), 55 psi Heater gas (GS2)-all the gas was nitrogen. The collision energy (CE) was set at -40 eV, and the declustering potential (DP) was -80 eV. The scan range was operated with the mass m/z 100 to m/z 1000.

Data analysis strategy
The absorbed prototype constituents were identified by Peakview 2.1 software. The group 4 (blank group) and herb solution were employed as negative control and positive controls, respectively, and the extracted ion peaks which detected in the dosed groups and herb solution but not in the blank group were identified as prototype compounds. Prototype compounds could be analyzed by retention time, accurate mass, and MS fragmentation patterns. We used a three-step strategy to identify metabolites in rat plasma as follows: (1) The bulk data-mining tools including XIC, MDF, PIF, and NLF of MetabolitePilot 2.0 software were employed for data post-processing. (2) Peakview 2.1 software was used to confirm metabolite structures. (3) Clog P, calculated by the software ChemDraw 19.0, was adopted to distinguish isomers. Usually, the value of Clog P is inversely related to retention time of the metabolites.

2.7
Target network pharmacology 2.7.1. Compound targets of RSLDP. Since the absorbed constituents in the plasma were more probably the bioactive compounds, the prototype components and metabolites identified by UHPLC-QTOF-MS were employed to construct the chemical information database of RSLDP for network pharmacology research. The prototype components were searched in the PubChem database and the metabolites were drawn by ChemDraw to obtain their molecular information including molecular formula and canonical SMILES. Then, the canonical SMILES were sent to the Swiss Target Prediction (http://www.swisstargetprediction.ch/), after which the species was set to "Homo sapiens".

Protein-protein interactions (PPIs).
The targets of active ingredients in RSLDP and COVID-19-related targets were used the Bioconductor (R) software (https:// bioconductor.org/, version 3.15, released on April 27, 2022) to obtain a Venn diagram and obtain the intersection of drug-related targets and disease-related targets. The above common targets were introduced into the STRING (https://cn.string-db.org/, version 11.5) database. For the newly constructed PPIs network, the condition was limited to "Homo sapiens" with high confidence (combine score > 0.90).

Network construction.
Network construction was used by the Cytoscape (version 3.9.1) software as follows: (1) Compounds-targets network; (2) PPIs network. Moreover, CytoNCA, a Cytoscape plugin, was applied to calculate the topological properties of the PPIs network to screen core targets based on Betweenness centrality (BC), Degree Centrality (DC), Closeness centrality (CC), and Eigenvector (EC).

GO and KEGG enrichment analyses.
The enrichment of gene ontology (GO) terms in the biological process, cellular component and molecular function categories, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed with R software. P value < 0.05 were selected for further analysis.

Results and discussion
In this study, a total of RS-related compounds, including 22 prototype compounds and 10 metabolites, were identified in group 1; a total of Licorice-related compounds, including 22 prototype compounds and 5 metabolites, were identified in group 2; and a total of RSL-related compounds, including 55 prototype compounds and 10 metabolites, were identified in group 3. The MS data are listed as Tables 1 and 2, and the structures of these compounds are shown in Fig 1. The total ion chromatogram of prototype components and metabolites is shown in Fig 2.

Identification of metabolites of typical constituents in rat plasma
In this work, we selected Wogonin, Wogonoside, and Isoliquiritigenin as examples to elucidate how the metabolites produced from the absorbed constituents. A total of 23 metabolites of

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The fragment ions at m/z 267.0664, 252.0426, and 223.0390 were formed by the loss of SO 3 , CH 3 , and CHO, which suggested that the formula was C 16

Compound targets of RSLDP
From the Swiss Target Prediction database, 440 targets were gathered corresponding to the selected 51 absorbed components (after removing ingredients without any relevant targets) and their 10 metabolites in RSLDP.

PPIs
A total of 106 overlapping targets were identified by intersecting of the drug-related targets with disease-related targets. In detail, 10 genes were only in Radix Scutellariae-COVID-19 cotargets, 32 genes were only in Licorice-COVID-19 co-targets, and 64 genes were both in Radix Scutellariae-COVID-19 co-targets and Licorice-COVID-19 co-targets ( Fig 7A). To obtain the potential relationship among the 106 targets of RSLDP against COVID-19, the PPIs were imported into the STRING database. PPIs containing 100 nodes and 489 edges were acquired (Fig 8). In the figures shown here (Fig 8), every circle expressed a target protein, and the center of the dot represented the protein structure.

Network construction
To clarify the potential mechanism of RSLDP in the treatment of COVID-19, Cytoscape v3.9.1 software was adopted to construct a compounds-targets network, as shown in Fig 7B. The red, teal circle stands for compounds of Radix Scutellariae and Licorice, respectively. The red and teal circle represents common compounds of Radix Scutellariae and Licorice. A blue rectangle is employed to stand for the target gene. And edges symbols targets interacting with them. As presented in Fig 7B, the network shows 167 nodes, containing 61 compounds, 106 target genes as well as 797 edges. The average degree value is 9.545, and the degree of 63 nodes was larger than 9.545. The results indicate that these nodes may be candidate active compounds and potential targets for RSLDP in the treatment of COVID-19.
We used Cytoscape v3.9.1 software to construct a PPIs network. (Fig 9) As shown in

Pathway enrichment analysis
To explore the functional distribution of RSLDP in the treatment of COVID-19, the 106 targets were sent to R software, and the enrichment analysis of GO and KEGG pathways was performed. As shown in Fig 10A, we selected 10 items of biological function, cell function, and molecular function in GO analysis, which involved protein kinase activity, response to chemical stress, response to oxygenates, positive regulation of cytokines, and other biological process functions. As shown in Fig 10B, it shows the first 20 signaling pathways, which relate to PI3K-Akt signaling pathway, AGE-RAGE signaling pathway for diabetic complications, virus-related pathways such as novel coronavirus and human cytomegalovirus infection, inflammatory immune-related pathways, and so on.
Traditional Chinese medicines (TCM) have the effect characteristics of multi-component, multi-pathway, and multi-target in treating diseases. For oral herbal drug pairs, it is commonly believed that their components must be absorbed into the blood to possess therapeutic effects. The holistic and systematic characteristics of target-network pharmacology correspond to the study content of "multi-component, multi-pathway, and multi-target" in TCM. The combination of blood components and target-network pharmacology is useful in identifying the absorbed ingredients of drug pairs and predicting their mechanism of action. Based on this approach, we combined the 61 blood components of RSLDP with its target-network pharmacology.
From the analysis results of prototype components of RSLDP before and after pairing, most of the components in the group of RSLDP can be found in the individually administered group, but 20 components were detected after the pairing that were not detected before the pairing. This may be the result of the interaction of the components of the herbs during the pairing process. It also indicates that the pairing promotes the selective absorption of these components. However, the research is just a preliminary study of the overall changes in the

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chemical composition of the RSLDP in vivo before and after the pairing. Therefore, we will explore the differences in the component content and metabolites in vivo before and after the pairing to more comprehensively explain the chemical mechanism of the pairing in future research.
From the results of target-network pharmacology, 61 compounds and 106 targets may be the bioactive compounds and pharmacological targets of RSLDP for the treatment of COVID-19. Further research revealed that seven key active components (chrysin, baicalein, wogonin, apigenin, isoliquiritigenin, isoliquiritin apioside, and Glycyrrhetinic acid) were regarded to be effective on COVID-19, and twelve targets (MAPK3, IL6, JUN, IL2, STAT3, AKT1, TNF, STAT1, EGFR, HSP90AA1, VEGFA RELA) were screened to be effective on COVID-19. Studies have shown that baicalein and apigenin can inhibit the 3C-like protease of SARS-CoV-2 [26]. Glycyrrhetinic acid has been shown to have anti-COVID-19 activity [27]. Isoliquiritin apioside is also a potential compound against COVID-19 [28]. IL-6 has been shown to be associated with COVID-19 [29]. It has been shown that JUN and AKT1 play an important role in COVID-19 [30,31]. Increased levels of TNF, a key pro-inflammatory cytokine, have been proven to be related to elevated mortality in COVID-19 [32]. STAT3 is a potential molecular target for clinical syndromes characterized by systemic inflammation in COVID-19 [33]. KEGG pathway analysis further indicated that the most important of RSLDP were PI3K-Akt signaling pathway, AGE-RAGE signaling pathway for diabetic complications, and inflammatory immune-related pathways. This result indicated that RSLDP exerts its therapeutic effect by modulating the immune response and inflammatory activation processes. The PI3K-Akt signaling pathway is involved in the pathogenesis of pulmonary fibrosis and in the immune response process of host cells to resist viral invasion, and this pathway is significant in the antiinflammatory effects of COVID-19 [34]. GO pathway analysis further indicated that the most important of RSLDP were protein kinase activity, response to chemical stress, response to oxygenates, positive regulation of cytokines, and other biological process functions. It has been shown that their responses may play a driving role in the development and progression of COVID-19, such as the cellular response to chemical stress, and response to oxygenated materials [35]. Though some results have been obtained from this study, there are still limitations. The targets of components should be further verified, including existing compounds and functional proteins.

Conclusion
In the current study, we combined UHPLC-QTOF-MS analysis and target network pharmacology to expose the potential pharmacological mechanisms of RSLDP against COVID-19. The identified compounds by UHPLC-QTOF-MS analysis provided a material basis for target network pharmacology. The results have shown that the effects of 61 active compounds of RSLDP on the 106 potential targets were related to COVID-19. Based on it, the twelve core targets (STAT3, AKT1, EGFR, HSP9AA1, MAPK3, JUN, IL6, VEGFA, TNF, IL2, RELA, and STAT1) could be the most important targets for RSLDP in the treatment of COVID-19. And it may play a therapeutic role via PI3K-Akt signaling pathway, AGE-RAGE signaling pathway for diabetic complications, virus-related pathways such as novel coronavirus infection, inflammatory immune-related pathways, and so on. Overall, this is the first report to employ UHPLC-QTOF-MS analysis on the metabolic pathways of RSLDP in vivo. The findings not only clarify the potential mechanisms of RSLDP in the treatment of COVID-19 but also provide a theoretical basis for the future clinical research of TCMs.